A survey of IoT security based on a layered architecture of sensing and data analysis

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond

Towards securing machine learning models against membership inference attacks

From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress in the research works proposing defence techniques against ML security and privacy attacks. The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks (MIA) and the related countermeasures. In this paper, we introduce a countermeasure against membership inference attacks (MIA) on Conventional Neural Networks (CNN) based on dropout and L2 regularization. Through experimental analysis, we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model. Indeed, using CNN model training on two datasets CIFAR-10 and CIFAR-100, we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers. Moreover, we present a solution to achieve a trade-off between the performance of the model and the mitigation of MIA attack

Intrusion detection systems for smart home IoT devices: experimental comparison study

Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the employment of such systems in the context of a smart home can be challenging due to the devices hardware limitations, which may restrict their ability to counter the existing and emerging attack vectors. Therefore, this paper proposes an experimental comparison between the widely used open-source NIDSs namely Snort, Suricata and Bro IDS to find the most appropriate one for smart homes in term of detection accuracy and resources consumption including CP and memory utilization. Experimental Results show that Suricata is the best performing NIDS for smart homesComment: 7 pages, 4 figures, 2 table

Public knowledge and behaviours relating to antibiotic use in Gulf Cooperation Council countries: A systematic review

© 2018 The Authors The aim of this review was to assess public knowledge and behaviours in relation to antibiotic use in GCC countries. A systematic review was performed using MEDLINE, EMBASE and other relevant databases. Cross-sectional studies published from January 2000 to June 2017 relating to public knowledge and behaviours towards antibiotic use were included. Overall nine studies met the inclusion criteria for this systematic review. Nearly half of general public respondents in the GCC region reported a lack of knowledge about antibiotic use and showed negative attitudes towards antibiotic utilisation. Penicillin was the most frequently misused antibiotic, particularly for self-medication. Most respondents declared that they obtained information on antibiotics from pharmacists. Pharmacies were the major source of antibiotics used for self-medication. A multi-disciplinary approach must be put in place to educate the public on appropriate antibiotic use, to improve policies regarding the rational prescription of antimicrobials and to increase regulation enforcement

Systematic Review of Medicine-Related Problems in Adult Patients with Atrial Fibrillation on Direct Oral Anticoagulants

New oral anticoagulant agents continue to emerge on the market and their safety requires assessment to provide evidence of their suitability for clinical use. There-fore, we searched standard databases to summarize the English language literature on medicine-related problems (MRPs) of direct oral anticoagulants DOACs (dabigtran, rivaroxban, apixban, and edoxban) in the treatment of adults with atri-al fibrillation. Electronic databases including Medline, Embase, International Pharmaceutical Abstract (IPA), Scopus, CINAHL, the Web of Science and Cochrane were searched from 2008 through 2016 for original articles. Studies pub-lished in English reporting MRPs of DOACs in adult patients with AF were in-cluded. Seventeen studies were identified using standardized protocols, and two reviewers serially abstracted data from each article. Most articles were inconclusive on major safety end points including major bleeding. Data on major safety end points were combined with efficacy. Most studies inconsistently reported adverse drug reactions and not adverse events or medication error, and no definitions were consistent across studies. Some harmful drug effects were not assessed in studies and may have been overlooked. Little evidence is provided on MRPs of DOACs in patients with AF and, therefore, further studies are needed to establish the safety of DOACs in real-life clinical practice

Depression among sickle cell anemia patients in the Eastern Province of Saudi Arabia

Objectives: To determine the prevalence of, and factors associated with, depression among sickle cell anemia adult patients in the Eastern Province of Saudi Arabia. Materials and Methods: A cross-sectional study was conducted between December 2014 and May 2015 among sickle cell anemia patients aged 16–70 years from the outpatient hematology clinics at Qatif Central Hospital. A total of 110 successive participants consented and answered an anonymous, self-administered, questionnaire and the Arabic version of the Beck Depression Inventory-II. Individuals were considered depressed if they scored ≥14 in Beck Depression Inventory-II. Simple logistic regression was used to compare differences between the depressed and nondepressed groups. Odds ratios (ORs) with 95% confidence intervals (95% CI) were reported. Results: Depression was detected in 53 participants (48.2%). Bivariate analysis showed that lower educational qualification (OR = 2.5; 95% CI = 1.1–5.3; P = 0.021), higher frequency of vaso-occlusive crises (OR = 3.4; 95% CI = 1.3–8.7; P = 0.008) and frequent visits to the hematology clinic (OR = 5.3; 95% CI = 1.4–19.9; P = 0.008) were significantly associated with depression. Conclusion: This study revealed that there is high prevalence of depression among sickle cell anemia patients in the Eastern Province of Saudi Arabia

Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU’s Blackboard learning management system. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model’s performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests

Performance Evaluation Study of Intrusion Detection Systems.

With the thriving technology and the great increase in the usage of computer networks, the risk of having these network to be under attacks have been increased. Number of techniques have been created and designed to help in detecting and/or preventing such attacks. One common technique is the use of Network Intrusion Detection / Prevention Systems NIDS. Today, number of open sources and commercial Intrusion Detection Systems are available to match enterprises requirements but the performance of these Intrusion Detection Systems is still the main concern. In this paper, we have tested and analyzed the performance of the well know IDS system Snort and the new coming IDS system Suricata. Both Snort and Suricata were implemented on three different platforms (ESXi virtual server, Linux 2.6 and FreeBSD) to simulate a real environment. Finally, in our results and analysis a comparison of the performance of the two IDS systems is provided along with some recommendations as to what and when will be the ideal environment for Snort and Suricata

近200年来关中地区干旱灾害时空变化研究

Adverse drug events (ADEs) impose a major clinical and cost burden on acute hospital services. It has been reported that medicines reconciliation provided by pharmacists is effective in minimizing the chances of hospital admissions related to adverse drug events.To update the previous assessment of pharmacist-led medication reconciliation by restricting the review to randomized controlled trials (RCTs) only.Six major online databases were sifted up to 30 December 2016, without inception date (Embase, Medline Ovid, PubMed, BioMed Central, Web of Science and Scopus) to assess the effect of pharmacist-led interventions on medication discrepancies, preventable adverse drug events, potential adverse drug events and healthcare utilization. The Cochrane tool was applied to evaluate the chances of bias. Meta-analysis was carried out using a random effects model.From 720 articles identified on initial searching, 18 RCTs (6,038 patients) were included. The quality of the included studies was variable. Pharmacists-led interventions led to an important decrease in favour of the intervention group, with a pooled risk ratio of 42% RR 0.58 (95% CI 0.49 to 0.67) P<0.00001 in medication discrepancy. Reductions in healthcare utilization by 22% RR 0.78 (95% CI 0.61 to 1.00) P = 0.05, potential ADEs by10% RR 0.90 (95% CI 0.78 to 1.03) P = 0.65 and preventable ADEs by 27% RR 0.73 (0.22 to 2.40) P = 0.60 were not considerable.Pharmacists-led interventions were effective in reducing medication discrepancies. However, these interventions did not lead to a significant reduction in potential and preventable ADEs and healthcare utilization